Register
Login
Resources
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Product
Data Engine LLMs Platform Enterprise
Pricing Explore
Connect to our Discord channel
Resources  
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Explore Pricing
Product  
Data Engine LLMs Platform Enterprise
Connect to Dagshub Discord channel
Register
Login

csinva
/
imodels
mirror of https://github.com/csinva/imodels

Watch 1
Issues   Discussions 0

prune.html 18 KB
Permalink History Raw

You have to be logged in to leave a comment. Sign In
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324 
  1. <!doctype html>
    2. 

  2. <html lang="en">
    3. 

  3. <head>
    4. 

  4. <meta charset="utf-8">
    5. 

  5. <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
    6. 

  6. <meta name="generator" content="pdoc 0.10.0" />
    7. 

  7. <link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
    8. 

  8. <link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
    9. 

  9. <link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
    10. 

  10. <style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
    11. 

  11. <style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
    12. 

  12. <style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
    13. 

  13. <script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
    14. 

  14. <script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
    15. 

  15. </head>
    16. 

  16. <body>
    17. 

  17. <main>
    18. 

  18. <article id="content">
    19. 

  19. <section id="section-intro">
    20. 

  20. <details class="source">
    21. 

  21. <summary>
    22. 

  22. <span>Expand source code</span>
    23. 

  23. </summary>
    24. 

  24. <pre><code class="python">from typing import List
    25. 

  25. from collections import Counter
    26. 

  26. 

  27. from imodels.util.rule import Rule
    28. 

  28. 

  29. 

  30. def prune_mins(rules: List[Rule], precision_min: float, recall_min: float) -&gt; List[Rule]:
    31. 

  31.     # Factorize rules before semantic tree filtering
    32. 

  32.     rules_ = [tuple(rule) for rule in rules]
    33. 

  33.     rules_dict = {}
    34. 

  34. 

  35.     # keep only rules verifying precision_min and recall_min:
    36. 

  36.     for rule, score in rules_:
    37. 

  37.         if score[0] &gt;= precision_min and score[1] &gt;= recall_min:
    38. 

  38.             if rule in rules_dict:
    39. 

  39.                 # update the score to the new mean
    40. 

  40.                 c = rules_dict[rule][2] + 1
    41. 

  41.                 b = rules_dict[rule][1] + 1. / c * (
    42. 

  42.                         score[1] - rules_dict[rule][1])
    43. 

  43.                 a = rules_dict[rule][0] + 1. / c * (
    44. 

  44.                         score[0] - rules_dict[rule][0])
    45. 

  45. 

  46.                 rules_dict[rule] = (a, b, c)
    47. 

  47.             else:
    48. 

  48.                 rules_dict[rule] = (score[0], score[1], 1)
    49. 

  49. 

  50.     rule_tuple_list = sorted(rules_dict.items(), key=lambda x: (x[1][0], x[1][1]), reverse=True)
    51. 

  51.     return [Rule(rule, args=scores) for rule, scores in rule_tuple_list]
    52. 

  52. 

  53. 

  54. def deduplicate(rules: List[Rule], max_depth_dup: int) -&gt; List[Rule]:
    55. 

  55.     if max_depth_dup is not None:
    56. 

  56.         rules = [max(rules_set, key=f1_score) for rules_set in find_similar_rulesets(rules, max_depth_dup)]
    57. 

  57.     return sorted(rules, key=lambda x: - f1_score(x))
    58. 

  58. 

  59. 

  60. def f1_score(rule: Rule) -&gt; float:
    61. 

  61.     return 2 * rule.args[0] * rule.args[1] / \
    62. 

  62.            (rule.args[0] + rule.args[1]) if (rule.args[0] + rule.args[1]) &gt; 0 else 0
    63. 

  63. 

  64. 

  65. def find_similar_rulesets(rules: List[Rule], max_depth_duplication: int = None) -&gt; List[List[Rule]]:
    66. 

  66.     &#34;&#34;&#34;Create clusters of rules using a decision tree based
    67. 

  67.     on the terms of the rules
    68. 

  68. 

  69.     Parameters
    70. 

  70.     ----------
    71. 

  71.     rules : List, List of rules
    72. 

  72.             The rules that should be splitted in subsets of similar rules
    73. 

  73. 

  74.     Returns
    75. 

  75.     -------
    76. 

  76.     rules : List of list of rules
    77. 

  77.             The different set of rules. Each set should be homogeneous
    78. 

  78. 

  79.     &#34;&#34;&#34;
    80. 

  80. 

  81.     def split_with_best_feature(rules, depth, exceptions=[]):
    82. 

  82.         &#34;&#34;&#34;
    83. 

  83.         Method to find a split of rules given most represented feature
    84. 

  84.         &#34;&#34;&#34;
    85. 

  85.         if depth == 0:
    86. 

  86.             return rules
    87. 

  87. 

  88.         rulelist = [rule.split(&#39; and &#39;) for rule, score in rules]
    89. 

  89.         terms = [t.split(&#39; &#39;)[0] for term in rulelist for t in term]
    90. 

  90.         counter = Counter(terms)
    91. 

  91.         # Drop exception list
    92. 

  92.         for exception in exceptions:
    93. 

  93.             del counter[exception]
    94. 

  94. 

  95.         if len(counter) == 0:
    96. 

  96.             return rules
    97. 

  97. 

  98.         most_represented_term = counter.most_common()[0][0]
    99. 

  99. 

  100.         # Proceed to split
    101. 

  101.         rules_splitted = [[], [], []]
    102. 

  102.         for rule in rules:
    103. 

  103.             if (most_represented_term + &#39; &lt;=&#39;) in rule.rule:
    104. 

  104.                 rules_splitted[0].append(rule)
    105. 

  105.             elif (most_represented_term + &#39; &gt;&#39;) in rule.rule:
    106. 

  106.                 rules_splitted[1].append(rule)
    107. 

  107.             else:
    108. 

  108.                 rules_splitted[2].append(rule)
    109. 

  109.         new_exceptions = exceptions + [most_represented_term]
    110. 

  110. 

  111.         # Choose best term
    112. 

  112.         return [split_with_best_feature(ruleset,
    113. 

  113.                                         depth - 1,
    114. 

  114.                                         exceptions=new_exceptions)
    115. 

  115.                 for ruleset in rules_splitted]
    116. 

  116. 

  117.     def breadth_first_search(rules, leaves=None):
    118. 

  118.         if len(rules) == 0 or not isinstance(rules[0], list):
    119. 

  119.             if len(rules) &gt; 0:
    120. 

  120.                 return leaves.append(rules)
    121. 

  121.         else:
    122. 

  122.             for rules_child in rules:
    123. 

  123.                 breadth_first_search(rules_child, leaves=leaves)
    124. 

  124.         return leaves
    125. 

  125. 

  126.     leaves = []
    127. 

  127.     res = split_with_best_feature(rules, max_depth_duplication)
    128. 

  128.     breadth_first_search(res, leaves=leaves)
    129. 

  129.     return leaves</code></pre>
    130. 

  130. </details>
    131. 

  131. </section>
    132. 

  132. <section>
    133. 

  133. </section>
    134. 

  134. <section>
    135. 

  135. </section>
    136. 

  136. <section>
    137. 

  137. <h2 class="section-title" id="header-functions">Functions</h2>
    138. 

  138. <dl>
    139. 

  139. <dt id="imodels.util.prune.deduplicate"><code class="name flex">
    140. 

  140. <span>def <span class="ident">deduplicate</span></span>(<span>rules: List[<a title="imodels.util.rule.Rule" href="rule.html#imodels.util.rule.Rule">Rule</a>], max_depth_dup: int) ‑> List[<a title="imodels.util.rule.Rule" href="rule.html#imodels.util.rule.Rule">Rule</a>]</span>
    141. 

  141. </code></dt>
    142. 

  142. <dd>
    143. 

  143. <div class="desc"></div>
    144. 

  144. <details class="source">
    145. 

  145. <summary>
    146. 

  146. <span>Expand source code</span>
    147. 

  147. </summary>
    148. 

  148. <pre><code class="python">def deduplicate(rules: List[Rule], max_depth_dup: int) -&gt; List[Rule]:
    149. 

  149.     if max_depth_dup is not None:
    150. 

  150.         rules = [max(rules_set, key=f1_score) for rules_set in find_similar_rulesets(rules, max_depth_dup)]
    151. 

  151.     return sorted(rules, key=lambda x: - f1_score(x))</code></pre>
    152. 

  152. </details>
    153. 

  153. </dd>
    154. 

  154. <dt id="imodels.util.prune.f1_score"><code class="name flex">
    155. 

  155. <span>def <span class="ident">f1_score</span></span>(<span>rule: <a title="imodels.util.rule.Rule" href="rule.html#imodels.util.rule.Rule">Rule</a>) ‑> float</span>
    156. 

  156. </code></dt>
    157. 

  157. <dd>
    158. 

  158. <div class="desc"></div>
    159. 

  159. <details class="source">
    160. 

  160. <summary>
    161. 

  161. <span>Expand source code</span>
    162. 

  162. </summary>
    163. 

  163. <pre><code class="python">def f1_score(rule: Rule) -&gt; float:
    164. 

  164.     return 2 * rule.args[0] * rule.args[1] / \
    165. 

  165.            (rule.args[0] + rule.args[1]) if (rule.args[0] + rule.args[1]) &gt; 0 else 0</code></pre>
    166. 

  166. </details>
    167. 

  167. </dd>
    168. 

  168. <dt id="imodels.util.prune.find_similar_rulesets"><code class="name flex">
    169. 

  169. <span>def <span class="ident">find_similar_rulesets</span></span>(<span>rules: List[<a title="imodels.util.rule.Rule" href="rule.html#imodels.util.rule.Rule">Rule</a>], max_depth_duplication: int = None) ‑> List[List[<a title="imodels.util.rule.Rule" href="rule.html#imodels.util.rule.Rule">Rule</a>]]</span>
    170. 

  170. </code></dt>
    171. 

  171. <dd>
    172. 

  172. <div class="desc"><p>Create clusters of rules using a decision tree based
    173. 

  173. on the terms of the rules</p>
    174. 

  174. <h2 id="parameters">Parameters</h2>
    175. 

  175. <dl>
    176. 

  176. <dt><strong><code>rules</code></strong> :&ensp;<code>List, List</code> of <code>rules</code></dt>
    177. 

  177. <dd>The rules that should be splitted in subsets of similar rules</dd>
    178. 

  178. </dl>
    179. 

  179. <h2 id="returns">Returns</h2>
    180. 

  180. <dl>
    181. 

  181. <dt><strong><code>rules</code></strong> :&ensp;<code>List</code> of <code>list</code> of <code>rules</code></dt>
    182. 

  182. <dd>The different set of rules. Each set should be homogeneous</dd>
    183. 

  183. </dl></div>
    184. 

  184. <details class="source">
    185. 

  185. <summary>
    186. 

  186. <span>Expand source code</span>
    187. 

  187. </summary>
    188. 

  188. <pre><code class="python">def find_similar_rulesets(rules: List[Rule], max_depth_duplication: int = None) -&gt; List[List[Rule]]:
    189. 

  189.     &#34;&#34;&#34;Create clusters of rules using a decision tree based
    190. 

  190.     on the terms of the rules
    191. 

  191. 

  192.     Parameters
    193. 

  193.     ----------
    194. 

  194.     rules : List, List of rules
    195. 

  195.             The rules that should be splitted in subsets of similar rules
    196. 

  196. 

  197.     Returns
    198. 

  198.     -------
    199. 

  199.     rules : List of list of rules
    200. 

  200.             The different set of rules. Each set should be homogeneous
    201. 

  201. 

  202.     &#34;&#34;&#34;
    203. 

  203. 

  204.     def split_with_best_feature(rules, depth, exceptions=[]):
    205. 

  205.         &#34;&#34;&#34;
    206. 

  206.         Method to find a split of rules given most represented feature
    207. 

  207.         &#34;&#34;&#34;
    208. 

  208.         if depth == 0:
    209. 

  209.             return rules
    210. 

  210. 

  211.         rulelist = [rule.split(&#39; and &#39;) for rule, score in rules]
    212. 

  212.         terms = [t.split(&#39; &#39;)[0] for term in rulelist for t in term]
    213. 

  213.         counter = Counter(terms)
    214. 

  214.         # Drop exception list
    215. 

  215.         for exception in exceptions:
    216. 

  216.             del counter[exception]
    217. 

  217. 

  218.         if len(counter) == 0:
    219. 

  219.             return rules
    220. 

  220. 

  221.         most_represented_term = counter.most_common()[0][0]
    222. 

  222. 

  223.         # Proceed to split
    224. 

  224.         rules_splitted = [[], [], []]
    225. 

  225.         for rule in rules:
    226. 

  226.             if (most_represented_term + &#39; &lt;=&#39;) in rule.rule:
    227. 

  227.                 rules_splitted[0].append(rule)
    228. 

  228.             elif (most_represented_term + &#39; &gt;&#39;) in rule.rule:
    229. 

  229.                 rules_splitted[1].append(rule)
    230. 

  230.             else:
    231. 

  231.                 rules_splitted[2].append(rule)
    232. 

  232.         new_exceptions = exceptions + [most_represented_term]
    233. 

  233. 

  234.         # Choose best term
    235. 

  235.         return [split_with_best_feature(ruleset,
    236. 

  236.                                         depth - 1,
    237. 

  237.                                         exceptions=new_exceptions)
    238. 

  238.                 for ruleset in rules_splitted]
    239. 

  239. 

  240.     def breadth_first_search(rules, leaves=None):
    241. 

  241.         if len(rules) == 0 or not isinstance(rules[0], list):
    242. 

  242.             if len(rules) &gt; 0:
    243. 

  243.                 return leaves.append(rules)
    244. 

  244.         else:
    245. 

  245.             for rules_child in rules:
    246. 

  246.                 breadth_first_search(rules_child, leaves=leaves)
    247. 

  247.         return leaves
    248. 

  248. 

  249.     leaves = []
    250. 

  250.     res = split_with_best_feature(rules, max_depth_duplication)
    251. 

  251.     breadth_first_search(res, leaves=leaves)
    252. 

  252.     return leaves</code></pre>
    253. 

  253. </details>
    254. 

  254. </dd>
    255. 

  255. <dt id="imodels.util.prune.prune_mins"><code class="name flex">
    256. 

  256. <span>def <span class="ident">prune_mins</span></span>(<span>rules: List[<a title="imodels.util.rule.Rule" href="rule.html#imodels.util.rule.Rule">Rule</a>], precision_min: float, recall_min: float) ‑> List[<a title="imodels.util.rule.Rule" href="rule.html#imodels.util.rule.Rule">Rule</a>]</span>
    257. 

  257. </code></dt>
    258. 

  258. <dd>
    259. 

  259. <div class="desc"></div>
    260. 

  260. <details class="source">
    261. 

  261. <summary>
    262. 

  262. <span>Expand source code</span>
    263. 

  263. </summary>
    264. 

  264. <pre><code class="python">def prune_mins(rules: List[Rule], precision_min: float, recall_min: float) -&gt; List[Rule]:
    265. 

  265.     # Factorize rules before semantic tree filtering
    266. 

  266.     rules_ = [tuple(rule) for rule in rules]
    267. 

  267.     rules_dict = {}
    268. 

  268. 

  269.     # keep only rules verifying precision_min and recall_min:
    270. 

  270.     for rule, score in rules_:
    271. 

  271.         if score[0] &gt;= precision_min and score[1] &gt;= recall_min:
    272. 

  272.             if rule in rules_dict:
    273. 

  273.                 # update the score to the new mean
    274. 

  274.                 c = rules_dict[rule][2] + 1
    275. 

  275.                 b = rules_dict[rule][1] + 1. / c * (
    276. 

  276.                         score[1] - rules_dict[rule][1])
    277. 

  277.                 a = rules_dict[rule][0] + 1. / c * (
    278. 

  278.                         score[0] - rules_dict[rule][0])
    279. 

  279. 

  280.                 rules_dict[rule] = (a, b, c)
    281. 

  281.             else:
    282. 

  282.                 rules_dict[rule] = (score[0], score[1], 1)
    283. 

  283. 

  284.     rule_tuple_list = sorted(rules_dict.items(), key=lambda x: (x[1][0], x[1][1]), reverse=True)
    285. 

  285.     return [Rule(rule, args=scores) for rule, scores in rule_tuple_list]</code></pre>
    286. 

  286. </details>
    287. 

  287. </dd>
    288. 

  288. </dl>
    289. 

  289. </section>
    290. 

  290. <section>
    291. 

  291. </section>
    292. 

  292. </article>
    293. 

  293. <nav id="sidebar">
    294. 

  294. <h1>Index 🔍</h1>
    295. 

  295. <div class="toc">
    296. 

  296. <ul></ul>
    297. 

  297. </div>
    298. 

  298. <ul id="index">
    299. 

  299. <li><h3>Super-module</h3>
    300. 

  300. <ul>
    301. 

  301. <li><code><a title="imodels.util" href="index.html">imodels.util</a></code></li>
    302. 

  302. </ul>
    303. 

  303. </li>
    304. 

  304. <li><h3><a href="#header-functions">Functions</a></h3>
    305. 

  305. <ul class="">
    306. 

  306. <li><code><a title="imodels.util.prune.deduplicate" href="#imodels.util.prune.deduplicate">deduplicate</a></code></li>
    307. 

  307. <li><code><a title="imodels.util.prune.f1_score" href="#imodels.util.prune.f1_score">f1_score</a></code></li>
    308. 

  308. <li><code><a title="imodels.util.prune.find_similar_rulesets" href="#imodels.util.prune.find_similar_rulesets">find_similar_rulesets</a></code></li>
    309. 

  309. <li><code><a title="imodels.util.prune.prune_mins" href="#imodels.util.prune.prune_mins">prune_mins</a></code></li>
    310. 

  310. </ul>
    311. 

  311. </li>
    312. 

  312. </ul>
    313. 

  313. <p><img align="center" width=100% src="https://csinva.io/imodels/img/anim.gif"> </img></p>
    314. 

  314. <!-- add wave animation -->
    315. 

  315. </nav>
    316. 

  316. </main>
    317. 

  317. <footer id="footer">
    318. 

  318. </footer>
    319. 

  319. </body>
    320. 

  320. </html>
    321. 

  321. <!-- add github corner -->
    322. 

  322. <a href="https://github.com/csinva/imodels" class="github-corner" aria-label="View source on GitHub"><svg width="120" height="120" viewBox="0 0 250 250" style="fill:#70B7FD; color:#fff; position: absolute; top: 0; border: 0; right: 0;" aria-hidden="true"><path d="M0,0 L115,115 L130,115 L142,142 L250,250 L250,0 Z"></path><path d="m128.3,109.0 c113.8,99.7 119.0,89.6 119.0,89.6 c122.0,82.7 120.5,78.6 120.5,78.6 c119.2,72.0 123.4,76.3 123.4,76.3 c127.3,80.9 125.5,87.3 125.5,87.3 c122.9,97.6 130.6,101.9 134.4,103.2" fill="currentcolor" style="transform-origin: 130px 106px;" class="octo-arm"></path><path d="M115.0,115.0 C114.9,115.1 118.7,116.5 119.8,115.4 L133.7,101.6 C136.9,99.2 139.9,98.4 142.2,98.6 C133.8,88.0 127.5,74.4 143.8,58.0 C148.5,53.4 154.0,51.2 159.7,51.0 C160.3,49.4 163.2,43.6 171.4,40.1 C171.4,40.1 176.1,42.5 178.8,56.2 C183.1,58.6 187.2,61.8 190.9,65.4 C194.5,69.0 197.7,73.2 200.1,77.6 C213.8,80.2 216.3,84.9 216.3,84.9 C212.7,93.1 206.9,96.0 205.4,96.6 C205.1,102.4 203.0,107.8 198.3,112.5 C181.9,128.9 168.3,122.5 157.7,114.1 C157.9,116.9 156.7,120.9 152.7,124.9 L141.0,136.5 C139.8,137.7 141.6,141.9 141.8,141.8 Z" fill="currentColor" class="octo-body"></path></svg></a><style>.github-corner:hover .octo-arm{animation:octocat-wave 560ms ease-in-out}@keyframes octocat-wave{0%,100%{transform:rotate(0)}20%,60%{transform:rotate(-25deg)}40%,80%{transform:rotate(10deg)}}@media (max-width:500px){.github-corner:hover .octo-arm{animation:none}.github-corner .octo-arm{animation:octocat-wave 560ms ease-in-out}}</style>
    323. 

  323. <!-- add wave animation stylesheet -->
    324. 

  324. <link rel="stylesheet" href="github.css">
    325.
Prev
Next
Tip!

Press p or to see the previous file or, n or to see the next file

Comments

Write Preview
Loading...
Integrate Google Cloud Storage
Use Google Storage
Select bucket
Upload key
Finish

Use Google Cloud Storage!

Browsing data directories saved to Google Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Google Storage bucket

Service Account Key

Congratulations!

imodels is now integrated with Google Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate AWS S3
Use S3 remote
Select bucket
Access key
Finish

Use AWS S3 as storage!

Browsing data directories saved to S3 is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Access key (If needed)

Congratulations!

imodels is now integrated with AWS S3!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate S3 compatible storage
Use S3 like remote
Select bucket
Access key
Finish

Use any S3 compatible storage!

Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Access key (If needed)

Congratulations!

imodels is now integrated with your S3 compatible storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate Azure Cloud Storage
Use Azure Storage
Select bucket
Set key
Finish

Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

imodels is now integrated with Azure Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes